Attribute Information:

Date Time: Each ten minutes. Temperature: Weather Temperature of Tetouan city. Humidity: Weather Humidity of Tetouan city. Wind Speed of Tetouan city. general diffuse flows diffuse flows power consumption of zone 1 of Tetouan city. power consumption of zone 2 of Tetouan city. power consumption of zone 3 of Tetouan city.

rm(list = ls()) #initialization
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
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## ✔ tibble  3.1.6     ✔ dplyr   1.0.8
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
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## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(data.table)
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library(lubridate)
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## Attaching package: 'lubridate'
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##     yday, year
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library(dplyr)
library(lubridate)
library(forecast)
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##   as.zoo.data.frame zoo
library(xts)
## Loading required package: zoo
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## Attaching package: 'zoo'
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## Attaching package: 'xts'
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library(zoo)
library(Metrics)
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library(ggplot2)
library(gplots)
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## Attaching package: 'gplots'
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library(RColorBrewer)
# read the real data on July 1 for test
df <- read.csv("Tetuan City power consumption.csv")
colnames(df)
## [1] "DateTime"                  "Temperature"              
## [3] "Humidity"                  "Wind.Speed"               
## [5] "general.diffuse.flows"     "diffuse.flows"            
## [7] "Zone.1.Power.Consumption"  "Zone.2..Power.Consumption"
## [9] "Zone.3..Power.Consumption"
# check for missing values
sum(is.na(df$Zone1))
## [1] 0
colnames(df)[7] <- "Zone1"
colnames(df)[8] <- "Zone2"
colnames(df)[9] <- "Zone3"
date <- seq(from=as.POSIXct("2017-01-01 00:00", format = "%Y-%m-%d %H:%M"), length.out = nrow(df), by = "10 min")
df$DateTime <- date #df(df$DateTime, format="%m/%d/%Y %H:%M")
#attr(df$DateTime, "tzone") <- "Africa/Casablanca"
df_xts <- xts(df[,7], date)

EDA

check stationarity

# ts plot and acf plot
var = colnames(df)
var = var[-1]
for(i in var) {       
  ts.plot(df[[i]], ylab=i)
  acf(df[[i]], ylab = paste0(i," ACF"), main="")
}

library(tseries)
for(i in var) {       
  print(adf.test(df[[i]])) # detects stationarity
  print(kpss.test(df[[i]],null = "Trend")) # detects trend stationarity
}
## Warning in adf.test(df[[i]]): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  df[[i]]
## Dickey-Fuller = -13.207, Lag order = 37, p-value = 0.01
## alternative hypothesis: stationary
## Warning in kpss.test(df[[i]], null = "Trend"): p-value smaller than printed p-
## value
## 
##  KPSS Test for Trend Stationarity
## 
## data:  df[[i]]
## KPSS Trend = 42.545, Truncation lag parameter = 19, p-value = 0.01
## Warning in adf.test(df[[i]]): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  df[[i]]
## Dickey-Fuller = -20.358, Lag order = 37, p-value = 0.01
## alternative hypothesis: stationary
## Warning in kpss.test(df[[i]], null = "Trend"): p-value smaller than printed p-
## value
## 
##  KPSS Test for Trend Stationarity
## 
## data:  df[[i]]
## KPSS Trend = 2.1044, Truncation lag parameter = 19, p-value = 0.01
## Warning in adf.test(df[[i]]): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  df[[i]]
## Dickey-Fuller = -7.717, Lag order = 37, p-value = 0.01
## alternative hypothesis: stationary
## Warning in kpss.test(df[[i]], null = "Trend"): p-value smaller than printed p-
## value
## 
##  KPSS Test for Trend Stationarity
## 
## data:  df[[i]]
## KPSS Trend = 16.004, Truncation lag parameter = 19, p-value = 0.01
## Warning in adf.test(df[[i]]): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  df[[i]]
## Dickey-Fuller = -35.446, Lag order = 37, p-value = 0.01
## alternative hypothesis: stationary
## Warning in kpss.test(df[[i]], null = "Trend"): p-value smaller than printed p-
## value
## 
##  KPSS Test for Trend Stationarity
## 
## data:  df[[i]]
## KPSS Trend = 4.9499, Truncation lag parameter = 19, p-value = 0.01
## Warning in adf.test(df[[i]]): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  df[[i]]
## Dickey-Fuller = -32.848, Lag order = 37, p-value = 0.01
## alternative hypothesis: stationary
## Warning in kpss.test(df[[i]], null = "Trend"): p-value smaller than printed p-
## value
## 
##  KPSS Test for Trend Stationarity
## 
## data:  df[[i]]
## KPSS Trend = 1.3615, Truncation lag parameter = 19, p-value = 0.01
## Warning in adf.test(df[[i]]): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  df[[i]]
## Dickey-Fuller = -32.63, Lag order = 37, p-value = 0.01
## alternative hypothesis: stationary
## Warning in kpss.test(df[[i]], null = "Trend"): p-value smaller than printed p-
## value
## 
##  KPSS Test for Trend Stationarity
## 
## data:  df[[i]]
## KPSS Trend = 6.6416, Truncation lag parameter = 19, p-value = 0.01
## Warning in adf.test(df[[i]]): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  df[[i]]
## Dickey-Fuller = -31.192, Lag order = 37, p-value = 0.01
## alternative hypothesis: stationary
## Warning in kpss.test(df[[i]], null = "Trend"): p-value smaller than printed p-
## value
## 
##  KPSS Test for Trend Stationarity
## 
## data:  df[[i]]
## KPSS Trend = 2.089, Truncation lag parameter = 19, p-value = 0.01
## Warning in adf.test(df[[i]]): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  df[[i]]
## Dickey-Fuller = -19.704, Lag order = 37, p-value = 0.01
## alternative hypothesis: stationary
## Warning in kpss.test(df[[i]], null = "Trend"): p-value smaller than printed p-
## value
## 
##  KPSS Test for Trend Stationarity
## 
## data:  df[[i]]
## KPSS Trend = 23.651, Truncation lag parameter = 19, p-value = 0.01
plot(df$Zone1, type = "l", xlab = "Time", ylab = "Watt Hours", main = "Power Consumption of Tetouan City", col = "#4F94CD")
lines(df$Zone2, col = "#708090")
lines(df$Zone3, col = "#EEE5DE")

legend("topleft", c("Zone 1", "Zone 2","Zone 3"),
       lty = c(1,1),
       col = c("#4F94CD","#708090", "#EEE5DE"))

Correlation

cor_df <- df[, c(2,3,4,5,6,7)]
cor_mat <- round(cor(cor_df),2)
heatmap.2(cor_mat,Colv=NA,Rowv=NA,col=brewer.pal(9,"Blues"),cellnote=cor_mat,notecol="white", na.color=par("bg"),trace='none',density.info='none', margins = c(5, 5),cexRow=0.5,cexCol=0.5)
## Warning in heatmap.2(cor_mat, Colv = NA, Rowv = NA, col = brewer.pal(9, :
## Discrepancy: Rowv is FALSE, while dendrogram is `both'. Omitting row dendogram.
## Warning in heatmap.2(cor_mat, Colv = NA, Rowv = NA, col = brewer.pal(9, :
## Discrepancy: Colv is FALSE, while dendrogram is `column'. Omitting column
## dendogram.

comp_daily <- decompose(ts(df$Zone1,frequency = 6*24))
plot(comp_daily)

comp_weekly <- decompose(ts(df$Zone1,frequency = 6*24*7))
plot(comp_weekly)

plot(df_xts['2017-01-01/2017-01-07'], type = "l", xlab = "Time", ylab = "Watt Hours", main = "One Week of Power Consumption of Zone 1 of Tetouan City ", col = "#4F94CD")

plot(df_xts['2017-01-01'], type = "l", xlab = "Time", ylab = "Watt Hours", main = "One Day of Power Consumption of Zone 1 of Tetouan City ", col = "#4F94CD")

plot(df_xts['2017-01-01/2017-01-30'], type = "l", xlab = "Time", ylab = "Watt Hours", main = "One Month of Power Consumption of Zone 1 of Tetouan City ", col = "#4F94CD")